Ensuring efficient and safe trajectory planning for UAVs in complex and dynamic environments is a critical challenge, especially for UAVs that are increasingly deployed in applications like environmental monitoring, disaster management, and surveillance. The primary complications in the safe control of UAVs include real-time obstacle avoidance, adaptation to unpredictable environmental changes, and coordination among multiple UAVs to prevent collisions. This paper addresses these challenges by proposing a novel approach for UAV trajectory planning that integrates obstacle avoidance and target acquisition. We introduce a new cost function designed to minimize the distance to the target while maximizing the distance from obstacles, effectively balancing these competing objectives to ensure safety and efficiency. To optimize this cost function, we employ the self-organizing migrating algorithm, a swarm intelligence algorithm inspired by the cooperative and competitive behaviors observed in natural organisms. Our method enables UAVs to autonomously generate safe and efficient paths in real-time, adapt to dynamic changes, and scale to large swarms without relying on centralized control. Simulation results across three scenarios-including a complex environment with ten UAVs and multiple obstacles-demonstrate the effectiveness of our approach. The UAVs successfully reach their targets while avoiding collisions, confirming the reliability and robustness of the proposed method. This work contributes to advancing autonomous UAV operations by providing a scalable and adaptable solution for trajectory planning in challenging environments.

• MeSH
• algoritmy * MeSH
• bezpilotní létající prostředky * MeSH
• počítačová simulace MeSH
• teoretické modely MeSH
• Publikační typ
• časopisecké články MeSH

Data analyses in particle physics rely on an accurate simulation of particle collisions and a detailed simulation of detector effects to extract physics knowledge from the recorded data. Event generators together with a geant-based simulation of the detectors are used to produce large samples of simulated events for analysis by the LHC experiments. These simulations come at a high computational cost, where the detector simulation and reconstruction algorithms have the largest CPU demands. This article describes how machine-learning (ML) techniques are used to reweight simulated samples obtained with a given set of parameters to samples with different parameters or samples obtained from entirely different simulation programs. The ML reweighting method avoids the need for simulating the detector response multiple times by incorporating the relevant information in a single sample through event weights. Results are presented for reweighting to model variations and higher-order calculations in simulated top quark pair production at the LHC. This ML-based reweighting is an important element of the future computing model of the CMS experiment and will facilitate precision measurements at the High-Luminosity LHC.

• Publikační typ
• časopisecké články MeSH

We present an overview of wildlife-vehicle collision (WVC) liability covering 36 European countries. We reviewed approaches to WVC liability which are currently in effect across Europe and their potential consequences for WVC reporting. To obtain relevant information, we conducted a survey, including a web-based questionnaire. We retrieved answers to questions related to human fatalities from WVC, the existence of WVC databases, roadkill data systems and recommendation for drivers in the event of WVC. In 19 countries, no one is liable when a motorized vehicle collides with a wild animal. In the remaining countries, road managers or road owners may be liable as well as drivers or hunters, either consistently or under certain conditions. Liability can, in some countries, be changed after a legal assessment. Human fatalities due to WVCs have been reported in 27 countries, with approximately 90 deaths annually across European roads. The number of injured people and estimates of socio-economic losses were not possible to obtain at a European level as many countries lack reliable databases. We discuss how existing WVC liability across countries provoke some actors to transfer liability to another actor or avoid reporting these incidents altogether. WVC underreporting in certain national databases is one of the consequences of the existing WVC liability rules in the given countries. This fact reduces the potential to identify hotspots and define appropriate mitigation measures. In conclusion, we propose several procedures for modifying WVC liability that could enhance wildlife protection and road safety.

• Klíčová slova
• Large mammals, Roadkill, Underreporting, Ungulates, WVC data, Wildlife management,
• MeSH
• divoká zvířata * MeSH
• dopravní nehody * zákonodárství a právo   MeSH
• lidé MeSH
• zvířata MeSH
• Check Tag
• lidé MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH
• Geografické názvy
• Evropa MeSH

BACKGROUND: Ice hockey is a dynamic game. We can observe collisions between the players that bring a risk of injury. There are many hockey clubs in the Czech Republic. These clubs raise great hockey players and many competing players in various levels of national leagues. The aim of this study was to map injuries in Czech hockey players and outline the situation of injury prevention and body care in ice hockey players. METHODS: We used a questionnaire survey method to obtain data. We received answers from 100 male active Czech ice hockey players, playing in the top three highest men's competitions (Extraleague - 2nd league). Individual injuries were analyzed according to specific body parts, injury type, playing position, level of competition using basic statistical characteristics and relative frequency analyses, including the recovery time, injury reason and the injury statistics per 1000 sporting performances in ice hockey. RESULTS: We found that 81% of participants suffered injuries with the overall incidence of injuries was 17.1 per 1000 sports performances and mainly happened during the match compared to training. The most common injuries were in the head and neck area (25%), often caused by a collision with another player, a stick or puck hit, or a collision with a board. Other frequently injured parts were the knees (21%), where internal ligament injuries predominate, and the shoulders (20%), where we recorded mainly ligament injuries. CONCLUSIONS: There is a high risk of various injury types of ice hockey players, that are developed accidentally in all body parts mostly in the match (mostly upper part of the body and knee) or by overloading (hip/groin area). We recommend strategies to avoid or minimize the injury risk of players. The hockey clubs, coaches, and players should extensively and regularly cooperate with physiotherapists, starting from the younger age of hockey groups, to prevent injuries and use regular strengthening of crucial muscle parts, regeneration, and compensatory exercises. We endorse adequately evaluating dangerous foul actions for referees and disciplinary officials also in minor competitions.

• MeSH
• dospělí MeSH
• hokej * zranění   MeSH
• incidence MeSH
• lidé MeSH
• mladý dospělý MeSH
• průzkumy a dotazníky MeSH
• sportovní úrazy * epidemiologie   prevence a kontrola   MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Česká republika epidemiologie   MeSH

Cooperative multi-agent systems make it possible to employ miniature robots in order to perform different experiments for data collection in wide open areas to physical interactions with test subjects in confined environments such as a hive. This paper proposes a new multi-agent path-planning approach to determine a set of trajectories where the agents do not collide with each other or any obstacle. The proposed algorithm leverages a risk-aware probabilistic roadmap algorithm to generate a map, employs node classification to delineate exploration regions, and incorporates a customized genetic framework to address the combinatorial optimization, with the ultimate goal of computing safe trajectories for the team. Furthermore, the proposed planning algorithm makes the agents explore all subdomains in the workspace together as a formation to allow the team to perform different tasks or collect multiple datasets for reliable localization or hazard detection. The objective function for minimization includes two major parts, the traveling distance of all the agents in the entire mission and the probability of collisions between the agents or agents with obstacles. A sampling method is used to determine the objective function considering the agents' dynamic behavior influenced by environmental disturbances and uncertainties. The algorithm's performance is evaluated for different group sizes by using a simulation environment, and two different benchmark scenarios are introduced to compare the exploration behavior. The proposed optimization method establishes stable and convergent properties regardless of the group size.

• Klíčová slova
• bio-hybrid systems, collision avoidance, genetic optimization, multi-agent, path planning, probabilistic roadmap,
• Publikační typ
• časopisecké články MeSH

This article proposes Persistence Administered Collective Navigation (PACNav) as an approach for achieving the decentralized collective navigation of unmanned aerial vehicle (UAV) swarms. The technique is based on the flocking and collective navigation behavior observed in natural swarms, such as cattle herds, bird flocks, and even large groups of humans. As global and concurrent information of all swarm members is not available in natural swarms, these systems use local observations to achieve the desired behavior. Similarly, PACNav relies only on local observations of the relative positions of UAVs, making it suitable for large swarms deprived of communication capabilities and external localization systems. We introduce the novel concepts ofpath persistenceandpath similaritythat allow each swarm member to analyze the motion of other members in order to determine its own future motion. PACNav is based on two main principles: (a) UAVs with little variation in motion direction have highpath persistence, and are considered by other UAVs to be reliable leaders; (b) groups of UAVs that move in a similar direction have highpath similarity, and such groups are assumed to contain a reliable leader. The proposed approach also embeds a reactive collision avoidance mechanism to avoid collisions with swarm members and environmental obstacles. This collision avoidance ensures safety while reducing deviations from the assigned path. Along with several simulated experiments, we present a real-world experiment in a natural forest, showcasing the validity and effectiveness of the proposed collective navigation approach in challenging environments. The source code is released as open-source, making it possible to replicate the obtained results and facilitate the continuation of research by the community.

• Klíčová slova
• decentralized control, multi-robot systems, relative localization, swarm robotics, unmanned aerial vehicles,
• MeSH
• bezpilotní létající prostředky MeSH
• komunikace MeSH
• letadla * MeSH
• lidé MeSH
• robotika * MeSH
• skot MeSH
• zvířata MeSH
• Check Tag
• lidé MeSH
• skot MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

In a collaborative scenario, the communication between humans and robots is a fundamental aspect to achieve good efficiency and ergonomics in the task execution. A lot of research has been made related to enabling a robot system to understand and predict human behaviour, allowing the robot to adapt its motion to avoid collisions with human workers. Assuming the production task has a high degree of variability, the robot's movements can be difficult to predict, leading to a feeling of anxiety in the worker when the robot changes its trajectory and approaches since the worker has no information about the planned movement of the robot. Additionally, without information about the robot's movement, the human worker cannot effectively plan own activity without forcing the robot to constantly replan its movement. We propose a novel approach to communicating the robot's intentions to a human worker. The improvement to the collaboration is presented by introducing haptic feedback devices, whose task is to notify the human worker about the currently planned robot's trajectory and changes in its status. In order to verify the effectiveness of the developed human-machine interface in the conditions of a shared collaborative workspace, a user study was designed and conducted among 16 participants, whose objective was to accurately recognise the goal position of the robot during its movement. Data collected during the experiment included both objective and subjective parameters. Statistically significant results of the experiment indicated that all the participants could improve their task completion time by over 45% and generally were more subjectively satisfied when completing the task with equipped haptic feedback devices. The results also suggest the usefulness of the developed notification system since it improved users' awareness about the motion plan of the robot.

• Klíčová slova
• bidirectional awareness, haptic feedback device, human machine interface, human robot collaboration, human robot interaction, path planning,
• MeSH
• ergonomie MeSH
• lidé MeSH
• pohyb těles MeSH
• robotika * MeSH
• uživatelské rozhraní počítače MeSH
• zpětná vazba MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

The border region between Austria, the Czech Republic, and Germany harbors the most south-western occurrence of moose in continental Europe. The population originated in Poland, where moose survived, immigrated from former Soviet Union or were reintroduced after the Second World War expanded west- and southwards. In recent years, the distribution of the nonetheless small Central European population seems to have declined, necessitating an evaluation of its current status. In this study, existing datasets of moose observations from 1958 to 2019 collected in the three countries were combined to create a database totaling 771 records (observations and deaths). The database was then used to analyze the following: (a) changes in moose distribution, (b) the most important mortality factors, and (c) the availability of suitable habitat as determined using a maximum entropy approach. The results showed a progressive increase in the number of moose observations after 1958, with peaks in the 1990s and around 2010, followed by a relatively steep drop after 2013. Mortality within the moose population was mostly due to human interactions, including 13 deadly wildlife-vehicle collisions, particularly on minor roads, and four animals that were either legally culled or poached. Our habitat model suggested that higher altitudes (ca. 700-1,000 m a.s.l.), especially those offering wetlands, broad-leaved forests and natural grasslands, are the preferred habitats of moose whereas steep slopes and areas of human activity are avoided. The habitat model also revealed the availability of large core areas of suitable habitat beyond the current distribution, suggesting that habitat was not the limiting factor explaining the moose distribution in the study area. Our findings call for immediate transboundary conservation measures to sustain the moose population, such as those aimed at preventing wildlife-vehicle collisions and illegal killings. Infrastructure planning and development activities must take into account the habitat requirements of moose.

• Klíčová slova
• Bohemian Forest Ecosystem, Habitat suitability modelling, Moose (alces alces),
• Publikační typ
• časopisecké články MeSH

OBJECTIVE: The liver is frequently injured in blunt abdominal trauma caused by road traffic accidents. The testing of safety performance of vehicles, e.g. belt usage, head support, seat shape, or air bag shape, material, pressure and reaction, could lead to reduction of the injury seriousness. Current trends in safety testing include development of accurate computational human body models (HBMs) based on the anatomical, morphological, and mechanical behavior of tissues under high strain. APPROACH: The aim of this study was to describe the internal pressure changes within porcine liver, the severity of liver injury and the relation between the porcine liver microstructure and rupture propagation in an experimental impact test. Porcine liver specimens (n = 24) were uniformly compressed using a drop tower technique and four impact heights (200, 300, 400 and 500 mm; corresponding velocities: 1.72, 2.17, 2.54 and 2.88 m s-1). The changes in intravascular pressure were measured via catheters placed in portal vein and caudate vena cava. The induced injuries were analyzed on the macroscopic level according to AAST grade and AIS severity. Rupture propagation with respect to liver microstructure was analyzed using stereological methods. MAIN RESULTS: Macroscopic ruptures affected mostly the interface between connective tissue surrounding big vessels and liver parenchyma. Histological analysis revealed that the ruptures avoided reticular fibers and interlobular septa made of connective tissue on the microscopic level. SIGNIFICANCE: The present findings can be used for evaluation of HBMs of liver behavior in impact situations.

• MeSH
• dopravní nehody MeSH
• játra zranění   MeSH
• lidé MeSH
• poranění břicha * MeSH
• prasata MeSH
• tlak MeSH
• tupá poranění * MeSH
• zvířata MeSH
• Check Tag
• lidé MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

In this paper, we propose an integrated biologically inspired visual collision avoidance approach that is deployed on a real hexapod walking robot. The proposed approach is based on the Lobula giant movement detector (LGMD), a neural network for looming stimuli detection that can be found in visual pathways of insects, such as locusts. Although a superior performance of the LGMD in the detection of intercepting objects has been shown in many collision avoiding scenarios, its direct integration with motion control is an unexplored topic. In our work, we propose to utilize the LGMD neural network for visual interception detection with a central pattern generator (CPG) for locomotion control of a hexapod walking robot that are combined in the controller based on the long short-term memory (LSTM) recurrent neural network. Moreover, we propose self-supervised learning of the integrated controller to autonomously find a suitable setting of the system using a realistic robotic simulator. Thus, individual neural networks are trained in a simulation to enhance the performance of the controller that is then experimentally verified with a real hexapod walking robot in both collision and interception avoidance scenario and navigation in a cluttered environment.

• MeSH
• chování zvířat fyziologie   MeSH
• chůze fyziologie   MeSH
• kobylky fyziologie   MeSH
• neuronové sítě MeSH
• řízené strojové učení MeSH
• robotika přístrojové vybavení   MeSH
• učení vyhýbat se fyziologie   MeSH
• zvířata MeSH
• Check Tag
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH